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@article{gnnSurveyZhou,
  author        = {Jie Zhou and
               Ganqu Cui and
               Zhengyan Zhang and
               Cheng Yang and
               Zhiyuan Liu and
               Maosong Sun},
  title         = {Graph Neural Networks: {A} Review of Methods and Applications},
  journal       = {CoRR},
  volume        = {abs/1812.08434},
  year          = {2018},
  url           = {http://arxiv.org/abs/1812.08434},
  archiveprefix = {arXiv},
  eprint        = {1812.08434},
  timestamp     = {Tue, 01 Dec 2020 13:54:50 +0100},
  biburl        = {https://dblp.org/rec/journals/corr/abs-1812-08434.bib},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}
@thesis{RConvKernel,
  title     = {Graph Kernels},
  publisher = {Ludwig-Maximilians-Universit{\"a}t M{\"u}nchen},
  author    = {Karsten Michael Borgwardt},
  year      = {2007},
  month     = {July},
  keywords  = {Graph Kernels, Graph Comparison, Kernel Methods, Feature Selection, Two-Sample-Test},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:19-71691}
}

@inproceedings{firstGNN,
  author    = {Scarselli, Franco and Tsoi, Ah Chung and Gori, Marco and Hagenbuchner, Markus},
  editor    = {Fred, Ana and Caelli, Terry M. and Duin, Robert P. W. and Campilho, Aur{\'e}lio C.and de Ridder, Dick},
  title     = {Graphical-Based Learning Environments for Pattern Recognition},
  booktitle = {Structural, Syntactic, and Statistical Pattern Recognition},
  year      = {2004},
  publisher = {Springer Berlin Heidelberg},
  address   = {Berlin, Heidelberg},
  pages     = {42--56},
  abstract  = {In this paper, we present a new neural network model, called graph neural network model, which is a generalization of two existing approaches, viz., the graph focused approach, and the node focused approach. The graph focused approach considers the mapping from a graph structure to a real vector, in which the mapping is independent of the particular node involved; while the node focused approach considers the mapping from a graph structure to a real vector, in which the mapping depends on the properties of the node involved. It is shown that the graph neural network model maintains some of the characteristics of the graph focused models and the node focused models respectively. A supervised learning algorithm is derived to estimate the parameters of the graph neural network model. Some experimental results are shown to validate the proposed learning algorithm, and demonstrate the generalization capability of the proposed model.},
  isbn      = {978-3-540-27868-9}
}

@inproceedings{GCNFourier,
  author    = {Joan Bruna and
               Wojciech Zaremba and
               Arthur Szlam and
               Yann LeCun},
  editor    = {Yoshua Bengio and
               Yann LeCun},
  title     = {Spectral Networks and Locally Connected Networks on Graphs},
  booktitle = {2nd International Conference on Learning Representations, {ICLR} 2014,
               Banff, AB, Canada, April 14-16, 2014, Conference Track Proceedings},
  year      = {2014},
  url       = {http://arxiv.org/abs/1312.6203},
  timestamp = {Thu, 04 Apr 2019 13:20:07 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/BrunaZSL13.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{GDVCounting,
  title   = {Graft: An Efficient Graphlet Counting Method for Large Graph Analysis},
  author  = {M. Rahman and Mansurul Bhuiyan and M. Hasan},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year    = {2014},
  volume  = {26},
  pages   = {2466-2478}
}

@article{DD,
  title   = {Distinguishing enzyme structures from non-enzymes without alignments.},
  author  = {P. Dobson and A. Doig},
  journal = {Journal of molecular biology},
  year    = {2003},
  volume  = {330 4},
  pages   = {
          771-83
        }
}

@article{MUTAG,
  author  = {Debnath, Asim Kumar and Lopez de Compadre, Rosa L. and Debnath, Gargi and Shusterman, Alan J. and Hansch, Corwin},
  title   = {Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. Correlation with molecular orbital energies and hydrophobicity},
  journal = {Journal of Medicinal Chemistry},
  volume  = {34},
  number  = {2},
  pages   = {786-797},
  year    = {1991},
  doi     = {10.1021/jm00106a046},
  url     = { 
        https://doi.org/10.1021/jm00106a046 },
  eprint  = { 
        https://doi.org/10.1021/jm00106a046}
}

@article{ComputationalComplexity,
  title   = {GRAPH KERNELS FOR DISEASE OUTCOME PREDICTION FROM PROTEIN-PROTEIN INTERACTION NETWORKS},
  isbn    = {9789812704177},
  url     = {https://www.research-collection.ethz.ch/handle/20.500.11850/96223},
  journal = {Biocomputing 2007},
  author  = {BORGWARDT, KARSTEN M. and KRIEGEL, HANS-PETER and VISHWANATHAN, S. V. N. and SCHRAUDOLPH, NICOL N.},
  year    = {2006}
}

@inbook{RepresenterTheorem,
  author    = {Wahba, Grace and Wang, Yuedong},
  publisher = {American Cancer Society},
  isbn      = {9781118445112},
  title     = {Representer Theorem},
  booktitle = {Wiley StatsRef: Statistics Reference Online},
  chapter   = {},
  pages     = {1-11},
  doi       = {https://doi.org/10.1002/9781118445112.stat08200},
  url       = {https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118445112.stat08200},
  eprint    = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/9781118445112.stat08200},
  year      = {2019},
  keywords  = {classification, functional data, nonparametric regression, penalized least squares, penalized likelihood, regularization, reproducing kernel Hilbert space, smoothing spline ANOVA, support vector machines},
}

@article{NCI1,
  author  = {Wale, Nikil and Watson, Ian and Karypis, George},
  year    = {2008},
  month   = {03},
  pages   = {347-375},
  title   = {Comparison of Descriptor Spaces for Chemical Compound Retrieval and Classification},
  volume  = {14},
  journal = {Knowl. Inf. Syst.},
  doi     = {10.1109/ICDM.2006.39}
}


@inproceedings{networkDataAnalytics,
  title     = {The Network Data Repository with Interactive Graph Analytics and Visualization},
  author    = {Ryan A. Rossi and Nesreen K. Ahmed},
  booktitle = { Association for the Advancement of Artificial Intelligence(AAAI)},
  url       = {http://networkrepository.com},
  year      = {2015}
}

@article{ChebyNet,
  author        = {Micha{\"{e}}l Defferrard and
               Xavier Bresson and
               Pierre Vandergheynst},
  title         = {Convolutional Neural Networks on Graphs with Fast Localized Spectral
               Filtering},
  journal       = {CoRR},
  volume        = {abs/1606.09375},
  year          = {2016},
  url           = {http://arxiv.org/abs/1606.09375},
  archiveprefix = {arXiv},
  eprint        = {1606.09375},
  timestamp     = {Mon, 13 Aug 2018 16:48:03 +0200},
  biburl        = {https://dblp.org/rec/journals/corr/DefferrardBV16.bib},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}

@article{1-OrderChebyNet,
  author        = {Thomas N. Kipf and
               Max Welling},
  title         = {Semi-Supervised Classification with Graph Convolutional Networks},
  journal       = {CoRR},
  volume        = {abs/1609.02907},
  year          = {2016},
  url           = {http://arxiv.org/abs/1609.02907},
  archiveprefix = {arXiv},
  eprint        = {1609.02907},
  timestamp     = {Mon, 13 Aug 2018 16:48:31 +0200},
  biburl        = {https://dblp.org/rec/journals/corr/KipfW16.bib},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}

@article{InductiveGCN,
  author        = {William L. Hamilton and
               Rex Ying and
               Jure Leskovec},
  title         = {Inductive Representation Learning on Large Graphs},
  journal       = {CoRR},
  volume        = {abs/1706.02216},
  year          = {2017},
  url           = {http://arxiv.org/abs/1706.02216},
  archiveprefix = {arXiv},
  eprint        = {1706.02216},
  timestamp     = {Mon, 13 Aug 2018 16:46:12 +0200},
  biburl        = {https://dblp.org/rec/journals/corr/HamiltonYL17.bib},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}

@misc{GAT,
  title         = {Graph Attention Networks},
  author        = {Petar Veličković and Guillem Cucurull and Arantxa Casanova and Adriana Romero and Pietro Liò and Yoshua Bengio},
  year          = {2018},
  eprint        = {1710.10903},
  archiveprefix = {arXiv},
  primaryclass  = {stat.ML}
}

@inproceedings{DiffPool,
  author    = {Ying, Zhitao and You, Jiaxuan and Morris, Christopher and Ren, Xiang and Hamilton, Will and Leskovec, Jure},
  booktitle = {Advances in Neural Information Processing Systems},
  editor    = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
  pages     = {},
  publisher = {Curran Associates, Inc.},
  title     = {Hierarchical Graph Representation Learning with Differentiable Pooling},
  url       = {https://proceedings.neurips.cc/paper/2018/file/e77dbaf6759253c7c6d0efc5690369c7-Paper.pdf},
  volume    = {31},
  year      = {2018}
}

@article{GraphUNets,
  author        = {Hongyang Gao and
               Shuiwang Ji},
  title         = {Graph U-Nets},
  journal       = {CoRR},
  volume        = {abs/1905.05178},
  year          = {2019},
  url           = {http://arxiv.org/abs/1905.05178},
  archiveprefix = {arXiv},
  eprint        = {1905.05178},
  timestamp     = {Tue, 28 May 2019 12:48:08 +0200},
  biburl        = {https://dblp.org/rec/journals/corr/abs-1905-05178.bib},
  bibsource     = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{KernelPDProof,
  author    = {Vert, Jean-Philippe},
  editor    = {Sakakibara, Yasubumi
and Kobayashi, Satoshi
and Sato, Kengo
and Nishino, Tetsuro
and Tomita, Etsuji},
  title     = {Classification of Biological Sequences with Kernel Methods},
  booktitle = {Grammatical Inference: Algorithms and Applications},
  year      = {2006},
  publisher = {Springer Berlin Heidelberg},
  address   = {Berlin, Heidelberg},
  pages     = {7--18},
  abstract  = {We survey the foundations of kernel methods and the recent developments of kernels for variable-length strings, in the context of biological sequence analysis.},
  isbn      = {978-3-540-45265-2}
}


@article{TopK,
  title        = {Self-Attention Graph Pooling},
  url          = {http://arxiv.org/abs/1904.08082},
  abstract     = {Advanced methods of applying deep learning to structured data such as graphs have been proposed in recent years. In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs. The method of generalizing the convolution operation to graphs has been proven to improve performance and is widely used. However, the method of applying downsampling to graphs is still difficult to perform and has room for improvement. In this paper, we propose a graph pooling method based on self-attention. Self-attention using graph convolution allows our pooling method to consider both node features and graph topology. To ensure a fair comparison, the same training procedures and model architectures were used for the existing pooling methods and our method. The experimental results demonstrate that our method achieves superior graph classification performance on the benchmark datasets using a reasonable number of parameters.},
  journaltitle = {{arXiv}:1904.08082 [cs, stat]},
  author       = {Lee, Junhyun and Lee, Inyeop and Kang, Jaewoo},
  urldate      = {2021-04-24},
  date         = {2019-06-13},
  eprinttype   = {arxiv},
  eprint       = {1904.08082},
  keywords     = {Computer Science - Machine Learning, Statistics - Machine Learning, Pooling, I.2.6},
  file         = {arXiv Fulltext PDF:/home/alkane/Zotero/storage/TQK6XQJQ/Lee 等。 - 2019 - Self-Attention Graph Pooling.pdf:application/pdf;arXiv.org Snapshot:/home/alkane/Zotero/storage/J83C9PB2/1904.html:text/html;全文:/home/alkane/Zotero/storage/5VQJ29YT/Lee 等。 - 2019 - Self-Attention Graph Pooling.pdf:application/pdf}
}

@misc{tu2020learning,
  title         = {Learning Features of Network Structures Using Graphlets},
  author        = {Kun Tu and Jian Li and Don Towsley and Dave Braines and Liam Turner},
  year          = {2020},
  eprint        = {1812.05473},
  archiveprefix = {arXiv},
  primaryclass  = {cs.SI}
}

@article{Ribeiro2017struc2vecLN,
  title   = {struc2vec: Learning Node Representations from Structural Identity},
  author  = {Leonardo F. R. Ribeiro and Pedro H. P. Saverese and Daniel R. Figueiredo},
  journal = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year    = {2017}
}

@article{Adhikari2018Sub2VecFL,
  title     = {Sub2Vec: Feature Learning for Subgraphs},
  author    = {B. Adhikari and Y. Zhang and Naren Ramakrishnan and B. A. Prakash},
  journal = {Pacific-Asia Conference on Knowledge Discovery and Data Mining(PAKDD)},
  year      = {2018}
}

@book{LearningGNN,
  title     = {深入浅出图神经网络},
  author    = {刘忠雨 and 李彦霖 and 周洋},
  year      = {2020},
  publisher = {机械工业出版社}
}

@book{GNN_basic_frontier,
  title     = {图神经网络——基础与前沿},
  author    = {马腾飞},
  date      = {2021},
  publisher = {电子工业出版社}
}

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